Quick yield ramp-up is critical for IC manufacturing. During a yield ramp-up process, yield learning methods may be employed to identify systematic yield limiters. As the circuit feature size continuously shrinks and the design complexity continuously increases, traditional yield learning methods such as inline inspection, memory bitmapping and test chips are becoming less effective. Recently, statistical yield learning methods based on volume diagnosis have been developed. These methods statistically analyze diagnosis results for a large number of failing devices to extract systematic issues and/or dominant defect mechanisms.
For practical applications of these statistical yield learning methods, high quality volume diagnosis needs to be accomplished with a reasonable amount of computational resources and within a reasonable amount of time. With the size of modern circuit designs increasing continuously, however, the time for diagnosing a single failing device keeps increasing. Moreover, the larger the circuit design for a failing device, the greater amount of physical memory required. For a circuit design with hundreds of millions of gates, for example, a diagnosis tool may require up to hundreds of giga-bytes of memory.
The volume diagnosis speed may be increased by equipping workstations with more processors and by improving the performance of diagnosis algorithms with various techniques such as pattern sampling, fault dictionary, and machine learning. On the other hand, the total amount of physical memory in a workstation cannot be increased as fast as the number processors. As a result, even for current workstations with the largest memory and tens of processors, a few diagnosis programs will use up all the memory and most of the processors will have to stay idle, limiting the number of concurrently running diagnosis programs. The low efficiency of resource utilization, in addition to the increasing processor time for each failing integrated circuit device (or failing die), presents a serious challenge to diagnosis throughput and thus to practical applications of the yield learning methods based on volume diagnosis.